Globally, universities are evaluating and targeting to reduce their carbon emissions and operate on a sustainable basis. The overall aim of this study revolves in addressing the following three questions: (1) How to calculate carbon footprint, including indicators selection, criteria, and measurement, for higher education institutions? (2) How to evaluate impact and effectiveness of various mitigation strategies in context of a higher education institution? (3) What are the possible limitations of approach selected for carbon footprint calculation. This paper presents estimation of the carbon footprint of NED University using a carbon calculator along with the identification of sources with maximum contribution to its carbon footprint. The carbon footprint of the NED University main campus for 2017 was calculated to be approximately 21,500 metric tons of equivalent CO₂ and carbon footprint per student was 1.79 metric tons of equivalent CO₂. Scope 1 and Scope 2 emissions each contributed nearly 7% of the carbon footprint, while Scope 3 emissions accounted for 85.6% of the carbon footprint. Major interventions such as switching to renewables, usage of energy efficient appliances, electric vehicles, and massive tree plantation inside and outside the campus were identified as the most effective mitigation strategies.

This paper presents an edge-deployable, real-time adaptive traffic light control system designed for complex and undisciplined urban traffic environments. The system integrates computer vision and Internet of Things (IoT) technologies to enable smart traffic management. A robust dataset was developed to capture diverse traffic conditions, including occlusions and unstructured vehicle movement. A lightweight vehicle detection model, based on (You Only Look Once) YOLO and deployed on a Jetson Xavier NX-powered roadside edge node (RSEN), enables real-time traffic monitoring with a mean Average Precision (mAP) of 90% and 74 frames per second (FPS). To improve traffic flow representation, we introduce a traffic density estimation method that incorporates Passenger Car Equivalent (PCE) factors, considering the varying impact of different vehicle types. The estimated density is used by an adaptive traffic light control (ATLC) algorithm, deployed on a resource-constrained Portenta H7 board, which dynamically adjusts signal timings using real-time data, starvation prevention, and tie-breaking mechanisms. The proposed system reduces traffic congestion at intersections by up to 33% and waiting times by 23% compared to traditional fixed-time controls. The proposed framework is cost-effective, scalable, and fully operational on edge devices, offering a practical solution for real-world smart city deployments.

The usage of BLDC motors in the low-power range is increasing rapidly in home appliances such as ceiling fans. This has necessitated the development of reliable, compact and efficient AC-DC power supplies for motor drive circuitry. This paper presents a power supply design consisting of an AC-DC isolated PFC Cuk converter with integrated magnetics that supplies a single-shunt voltage source inverter for the sensorless drive of the BLDC fan motor. The proposed power supply design is comprised of an integrated magnetics structure in which the two inductors and the transformer windings share the same core. The zero input and output ripple current conditions have been derived from the reluctance model of the magnetic assembly. Smooth operation of the motor by minimizing the motor torque ripples is evident from the results. The Cuk converter operates in continuous conduction mode (CCM), employing the current multiplier method. The CCM-based current multiplier control loop ensures a near-unity power factor as well as low total harmonic distortion in the supply current. The current loop also provides over-current protection, enhancing reliability of the system. Moreover, the speed of the BLDC motor is controlled by the field oriented control (FOC) algorithm, which enables direct operation with alternate energy sources such as batteries and solar photovoltaic panels. The performance of the proposed supply is validated: motor torque ripple is reduced to only 2.14% while maintaining 0.999 power factor and only 4.72% THD at full load. Failure modes analysis has also been performed through software simulations, using the PLECS simulation environment. Due to the reliable power supply design with low ripples, it is well suited for low-power BLDC motors in home appliances and small power tools, in addition to ceiling fans.

This paper presents a deep learning-based framework for automating the visual inspection of plastic bottles in an Industry 4.0 context, detecting surface defects to enhance product quality. Our contributions include the acceleration of model development through knowledge transfer learning, an inventive data generation strategy that combines physical samples with synthetic data augmentation techniques, an extensive evaluation of pre-trained deep convolutional neural networks, and a user-friendly interface for real-time quality inspection reporting and making the information easily accessible and actionable. In comparison to existing methods, our proposed method outperforms with a higher Accuracy to Size Ratio of 7.0. This characteristic underscores its capacity to efficiently and accurately classify and detect defects across multiple classes while maintaining a low area utilization. This feature not only demonstrates its exceptional performance but also positions it as a practical solution for real-world scenarios with resource constraints.

With the increasing utilization of plastic bottles in the fast-moving consumer goods industry, the efficiency and accuracy of the bottle defect inspection process becomes very important for quality assurance. Deep-learning-based inspection is accurate and robust, but at the same time data hogging and computationally expensive. Thus, it is not feasible for fast inspection. Therefore, this paper presents an efficient and fast machine-vision-based system for inspecting plastic bottle defects. The system is composed of a chamber which has a camera and illuminators to capture high-resolution images in controlled lighting conditions. Captured images are processed by using simple image processing techniques to identify multiple defects such as seated cap, dents on the body and label alignment on the plastic. The experimental results show that the proposed system is 95% accurate in determining a range of bottle defects. It is highly feasible for fast inspection and does not require high computation power and a large amount of training data.

Energy management is important for both consumers and utility providers. Utility providers are concerned with identifying and reducing energy wastage and thefts. Consumers are interested in reducing their energy consumption and bills. In Pakistan, residential and industrial estates account for nearly 31,000 MW of the maximum total demand, while the transmission and distribution capacity has stalled at about 22,000 MW. This 9000 MW gap in demand and supply, as reported in 2022, has led to frequent load shedding. Although the country now has an excess generation capacity of about 45,000 MW, the aging transmission and distribution network cannot deliver the requisite power at all times. Hence, electricity-related problems are likely to continue for the next few years in the country and the same is true for other low- and middle-income countries (LMICs). Several energy monitoring systems (EnMS) have been proposed, but they face limitations in terms of cost, ease of application, lack of universal installation capability, customization, and data security. The research below focused on the development of an economical, secure, and customizable real-time EnMS. The proposed EnMS comprises low-cost hardware for gathering energy data with universal compatibility, a secured communication module for real-time data transmission, and a dashboard application for visualization of real-time energy consumption in a user-preferred manner, making the information easily accessible and actionable. The experimental results and analysis revealed that approximately 40% cost savings in EnMS development could be achieved compared to other commercially available EnMSs. The performance of the EnMS hardware was evaluated and validated through rigorous on-site experiments. The front-end of the EnMS was assessed through surveys and was found to be interactive and user-friendly for the target clients. The developed EnMS architecture was found to be an economical end-product and an appropriate approach for small and medium clients such as residential, institutional, commercial, and industrial consumers, all on one platform.